In computer vision and image processing a neighborhood operation is a commonly used class of computations on image data which implies that it is processed according to the following pseudo code: Visit each point p in the image data and do { N = a neighborhood or region of the image data around the point p result(p) = f(N) } This general procedure can be applied to image data of arbitrary dimensionality. Also, the image data on which the operation is applied does not have to be defined in terms of intensity or color, it can be any type of information which is organized as a function of spatial (and possibly temporal) variables in p. The result of applying a neighborhood operation on an image is again something which can be interpreted as an image, it has the same dimension as the original data. The value at each image point, however, does not have to be directly related to intensity or color. Instead it is an element in the range of the function f, which can be of arbitrary type. Normally the neighborhood N is of fixed size and is a square (or a cube, depending on the dimensionality of the image data) centered on the point p. Also the function f is fixed, but may in some cases have parameters which can vary with p, see below. In the simplest case, the neighborhood N may be only a single point. This type of operation is often referred to as a point-wise operation. == Examples == The most common examples of a neighborhood operation use a fixed function f which in addition is linear, that is, the computation consists of a linear shift invariant operation. In this case, the neighborhood operation corresponds to the convolution operation. A typical example is convolution with a low-pass filter, where the result can be interpreted in terms of local averages of the image data around each image point. Other examples are computation of local derivatives of the image data. It is also rather common to use a fixed but non-linear function f. This includes median filtering, and computation of local variances. The Nagao-Matsuyama filter is an example of a complex local neighbourhood operation that uses variance as an indicator of the uniformity within a pixel group. The result is similar to a convolution with a low-pass filter with the added effect of preserving sharp edges. There is also a class of neighborhood operations in which the function f has additional parameters which can vary with p: Visit each point p in the image data and do { N = a neighborhood or region of the image data around the point p result(p) = f(N, parameters(p)) } This implies that the result is not shift invariant. Examples are adaptive Wiener filters. == Implementation aspects == The pseudo code given above suggests that a neighborhood operation is implemented in terms of an outer loop over all image points. However, since the results are independent, the image points can be visited in arbitrary order, or can even be processed in parallel. Furthermore, in the case of linear shift-invariant operations, the computation of f at each point implies a summation of products between the image data and the filter coefficients. The implementation of this neighborhood operation can then be made by having the summation loop outside the loop over all image points. An important issue related to neighborhood operation is how to deal with the fact that the neighborhood N becomes more or less undefined for points p close to the edge or border of the image data. Several strategies have been proposed: Compute result only for points p for which the corresponding neighborhood is well-defined. This implies that the output image will be somewhat smaller than the input image. Zero padding: Extend the input image sufficiently by adding extra points outside the original image which are set to zero. The loops over the image points described above visit only the original image points. Border extension: Extend the input image sufficiently by adding extra points outside the original image which are set to the image value at the closest image point. The loops over the image points described above visit only the original image points. Mirror extension: Extend the image sufficiently much by mirroring the image at the image boundaries. This method is less sensitive to local variations at the image boundary than border extension. Wrapping: The image is tiled, so that going off one edge wraps around to the opposite side of the image. This method assumes that the image is largely homogeneous, for example a stochastic image texture without large textons.
Adrozek
Adrozek is malware that injects fake ads into online search results. Microsoft announced the malware threat on 10 December 2020, and noted that many different browsers are affected, including Google Chrome, Microsoft Edge, Mozilla Firefox and Yandex Browser. The malware was first detected in May 2020 and, at its peak in August 2020, controlled over 30,000 devices a day. But during the December 2020 announcement, Microsoft claimed "hundreds of thousands" of infected devices worldwide between May and September 2020. According to Microsoft, if not detected and blocked, Adrozek adds browser extensions, modifies a specific DLL per target browser, and changes browser settings to insert additional, unauthorized ads into web pages, often on top of legitimate ads from search engines. For each user tricked into clicking on the fake ads, the scammers earn affiliate advertising dollars. The malware has been observed to extract device data and, in some cases, steal credentials, sending them to remote servers. Users may unintentionally install the malware because of a drive-by download, by visiting a tampered website, opening an e-mail attachment, or clicking on a deceptive link or a deceptive pop-up window. The main malware program is downloaded to the “Programs Files” folder using file names such as Audiolava.exe, QuickAudio.exe, and converter.exe. According to PC Magazine, a good way to avoid, or mitigate, infection by Adrozek is to keep browser and related software programs up to date.
Law and Corpus Linguistics
Law and corpus linguistics (LCL) is an academic sub-discipline that uses large databases of examples of language usage equipped with tools designed by linguists called corpora to better get at the meaning of words and phrases in legal texts (statutes, constitutions, contracts, etc.). Thus, LCL is the application of corpus linguistic tools, theories, and methodologies to issues of legal interpretation in much the same way law and economics is the application of economic tools, theories, and methodologies to various legal issues. == History == A 2005 law review article by Lawrence Solan noted in passing that corpus linguistics had potential for its application to interpreting legal texts. But the first systematic exploration and advocacy of applying the tools and methodologies of corpus linguistics to legal interpretive questions of law and corpus linguistics came in the fall of 2010, when the BYU Law Review published a note by Stephen Mouritsen, entitled The Dictionary is Not a Fortress: Definitional Fallacies and a Corpus-Based Approach to Plain Meaning. The note argued that dictionaries are the primary linguistic tool used by judges to determine the plain or ordinary meaning of words and phrases, and highlighted the deficiencies of such an approach. In its stead, the note proposed using corpus linguistics. And the note would be later cited by Adam Liptak in a New York Times article on statutory construction. Law and corpus linguistics (LCL) gained greater legitimacy in July 2011 with the first judicial opinion in American history utilizing corpus linguistics to determine the meaning of a legal text: In re the Adoption of Baby E.Z. In a concurrence in part and in the judgment, Justice Thomas Lee wrote to put forth an alternative ground for the majority's holding—interpreting the phrase "custody determination" by using corpus linguistics. Justice Lee looked at 500 randomized sample sentences from the Corpus of Contemporary American English (COCA) and found that the most common sense of "custody" was in the context of divorce rather than adoption. Further, he found that "custody" is ten times more likely to co-occur (or collocate) with "divorce" than with "adoption". From that evidence Justice Lee concluded that he "would find that the custody proceedings covered by the Act are limited to proceedings resulting in the modifiable custody orders of a divorce", rather than the broader range of custody proceedings. Other jurisprudence and scholarship would follow. In a 2015 concurrence in State v. Rasabout, Justice Lee used a COCA search to determine that "discharge" when used with a firearm (or one of its synonyms) overwhelmingly referred to a single shot rather than emptying the entire magazine of the weapon. And in 2016, four of the five justices joined a footnote in a majority opinion by Justice Lee commending a party for using corpus linguistics in its briefing even though the Court found it unnecessary to resolve the related question. Finally, in 2016 the Michigan Supreme Court became the first court to use a linguist-designed corpus in a majority opinion (COCA), with both the majority and the dissent turning to COCA to determine the meaning of the word "information". In 2020, courts desiring to bolster the legal theory of original intent have sought the opportunity to undertake analyses of statutes utilizing corpus linguistics. In a Ninth Circuit Court of Appeals case, Jones v. Becerra (No. 20-56174), a case involving the Second Amendment and the constitutionality of a California statute which bans the sale of firearms to individuals under the age of 21, a Ninth Circuit panel requested that the parties address three questions: 1) “What is the original public meaning of the Second Amendment phrases: ‘A well regulated Militia’; ‘the right of the people’; and ‘shall not be infringed’? 2) How does the tool of corpus linguistics help inform the determination of the original public meaning of those Second Amendment phrases?” 3) How do the data yielded from corpus linguistics assist in the interpretation of the constitutionality of age-based restrictions under the Second Amendment? As to scholarship, in 2012, Mouritsen followed up his original work with an article in the Columbia Science and Technology Law Review, where he further refined and promoted the use of corpus-based methods for determining questions of legal ambiguity. Additionally, in 2016 two essays and an article on law and corpus linguistics were published. The Yale Law Journal Forum published Corpus Linguistics & Original Public Meaning: A New Tool to Make Originalism More Empirical. Written by Justice Lee and two co-authors, the essay urged originalists to turn to corpus linguistics to improve the rigor and accuracy of originalist scholarship. And in response, the Forum published an essay by Lawrence Solan (a Brooklyn Law professor with a PhD in linguistics), Can Corpus Linguistics Help Make Originalism Scientific? The Boston University Public Interest Law Journal published The Merciful Corpus: The Rule of Lenity, Ambiguity and Corpus Linguistics by Daniel Ortner. In the article Ortner applied corpus linguistics to determining whether sufficient ambiguity exists to trigger the rule of lenity in five Supreme Court cases. Looking forward, in 2017 two more articles are slated for publication. Lee Strang focuses on corpus linguistics and originalism in the U.C. Davis Law Review, and Lawrence Solan and Tammy Gales explore corpus linguistics in the context of finding ordinary meaning in statutory interpretation in the International Journal of Legal Discourse. Lawyers and journalists have also taken notice of corpus linguistics at it relates to the law. In 2010, Neal Goldfarb filed the first known brief in the Supreme Court using corpus linguistics (COCA) to determine whether the ordinary meaning of "personal" referred to corporations in the case FCC v. AT&T. The amicus brief looked at the top collocates (words that co-occur) of "personal" in COHA as well as BYU's Time Magazine Corpus. And writing for The Atlantic, Ben Zimmer took note of this new trend, referring to corpus linguistics in the courts as "Like Lexis on Steroids". On the academic front, in 2013 BYU Law School started the first class on law and corpus linguistics, co-taught by Mouritsen, Lee, and (now Dean) Gordon Smith. The class is currently in its fourth year. And in February 2016, BYU Law School hosted the inaugural conference on LCL, with over two dozen legal and linguistic scholars from around the country discussing and debating the next steps forward for the growing academic movement. The conference has been held regularly in subsequent years. At the 2016 conference BYU Law School announced its plans and progress on the Corpus of Founding Era American English (COFEA), a corpus that covers 1760–1799 and contains more than 120 million words have been collected from founding era letters, diaries, newspapers, non-fiction books, fiction, sermons, speeches, debates, legal cases, and other legal materials.
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Mehryar Mohri
Mehryar Mohri is a professor and theoretical computer scientist at the Courant Institute of Mathematical Sciences. He is also heading the Machine Learning Theory (ML Theory) team at Google Research. == Career == Prior to joining the Courant Institute, Mohri was a research department head and later technology leader at AT&T Bell Labs, where he was a member of the technical staff for about ten years. Mohri has also taught as an assistant professor at the University of Paris 7 (1992-1993) and Ecole Polytechnique (1992-1994). == Research == Mohri's main area of research is machine learning, in particular learning theory. He is also an expert in automata theory and algorithms. He is the author of several core algorithms that have served as the foundation for the design of many deployed speech recognition and natural language processing systems. == Publications == Mohri is the author of the reference book Foundations of Machine Learning used as a textbook in many graduate-level machine learning courses. Mohri is also a member of the Lothaire group of mathematicians with the pseudonym M. Lothaire and contributed to the book on Applied Combinatorics on Words. He is the author of more than 250 conference and journal publications. == Organizational affiliations == Mohri is currently the President of the Association for Algorithmic Learning Theory (AALT) and the Steering Committee Chair for the ALT conference. He is also Editorial Board member of Machine Learning and TheoretiCS, Action Editor of the Journal of Machine Learning Research (JMLR) and a member of the advisory board for the Journal of Automata, Languages and Combinatorics.
Display list
A display list, also called a command list in Direct3D 12 and a command buffer in Vulkan, is a series of graphics commands or instructions that are run when the list is executed. Systems that make use of display list functionality are called retained mode systems, while systems that do not are as opposed to immediate mode systems. In OpenGL, display lists are useful to redraw the same geometry or apply a set of state changes multiple times. This benefit is also used with Direct3D 12's bundle command lists. In Direct3D 12 and Vulkan, display lists are regularly used for per-frame recording and execution. == Origins in vector displays == The vector monitors or calligraphic displays of the 1960s and 1970s used electron beam deflection to draw line segments, points, and sometimes curves directly on a CRT screen. Because the image would immediately fade, it needed to be redrawn many times a second (storage tube CRTs retained the image until blanked, but they were unsuitable for interactive graphics). To refresh the display, a dedicated CPU called a Display Processor or Display Processing Unit (DPU) was used, which had a memory buffer for a "display list", "display file", or "display program" containing line segment coordinates and other information. Advanced Display Processors also supported control flow instructions, which were useful for drawing repetitive graphics such as text, and some could perform coordinate transformations such as 3D projection. == Home computer display list functionality == One of the earliest systems with a true display list was the Atari 8-bit computers. The display list (actually called so in Atari terminology) is a series of instructions for ANTIC, the video co-processor used in these machines. This program, stored in the computer's memory and executed by ANTIC in real-time, can specify blank lines, any of six text modes and eight graphics modes, which sections of the screen can be horizontally or vertically fine-scrolled, and trigger Display List Interrupts (called raster interrupts or HBI on other systems). The Amstrad PCW family contains a Display List function called the 'Roller RAM'. This is a 512-byte RAM area consisting of 256 16-bit pointers in RAM, one for each line of the 720 × 256 pixel display. Each pointer identifies the location of 90 bytes of monochrome pixels that hold the line's 720 pixel states. The 90 bytes of 8 pixel states are spaced at 8-byte intervals, so there are 7 unused bytes between each byte of pixel data. This suits how the text-orientated PCW constructs a typical screen buffer in RAM, where the first character's 8 rows are stored in the first 8 bytes, the second character's rows in the next 8 bytes, and so on. The Roller RAM was implemented to speed up display scrolling as it would have been unacceptably slow for its 3.4 MHz Z80 to move up the 23 KB display buffer 'by hand' i.e. in software. The Roller RAM starting entry used at the beginning of a screen refresh is controlled by a Z80-writable I/O register. Therefore, the screen can be scrolled simply by changing this I/O register. Another system using a Display List-like feature in hardware is the Amiga, which, not coincidentally, was also designed by some of the same people who developed the custom hardware for the Atari 8-bit computers. Once directed to produce a display mode, it would continue to do so automatically for every following scan line. The computer also included a dedicated co-processor, called "Copper", which ran a simple program or 'Copper List' intended for modifying hardware registers in sync with the display. The Copper List instructions could direct the Copper to wait for the display to reach a specific position on the screen, and then change the contents of hardware registers. In effect, it was a processor dedicated to servicing raster interrupts. The Copper was used by Workbench to mix multiple display modes (multiple resolutions and color palettes on the monitor at the same time), and by numerous programs to create rainbow and gradient effects on the screen. The Amiga Copper was also capable of reconfiguring the sprite engine mid-frame, with only one scanline of delay. This allowed the Amiga to draw more than its 8 hardware sprites, so long as the additional sprites did not share scanlines (or the one scanline gap) with more than 7 other sprites. i.e., so long as at least one sprite had finished drawing, another sprite could be added below it on the screen. Additionally, the later 32-bit AGA chipset allowed the drawing of bigger sprites (more pixels per row) while retaining the same multiplexing. The Amiga also had dedicated block-shifter ("blitter") hardware, which could draw larger objects into a framebuffer. This was often used in place of, or in addition to, sprites. In more primitive systems, the results of a display list can be simulated, though at the cost of CPU-intensive writes to certain display modes, color control, or other visual effect registers in the video device, rather than a series of rendering commands executed by the device. Thus, one must create the displayed image using some other rendering process, either before or while the CPU-driven display generation executes. In many cases, the image is also modified or re-rendered between frames. The image is then displayed in various ways, depending on the exact way in which the CPU-driven display code is implemented. Examples of the results possible on these older machines requiring CPU-driven video include effects such as Commodore 64/128's FLI mode, or Rainbow Processing on the ZX Spectrum. == Usage in OpenGL == To delimit a display list, the glNewList and glEndList functions are used, and to execute the list, the glCallList function is used. Almost all rendering commands that occur between the function calls are stored in the display list. Commands that affect the client state are not stored in display lists. Display lists are named with an integer value, and creating a display list with the same name as one already created overrides the first. The glNewList function expects two arguments: an integer representing the name of the list, and an enumeration for the compilation mode. The two modes include GL_COMPILE_AND_EXECUTE, which compiles and immediately executes, and GL_COMPILE, which only compiles the list. Display lists enable the use of the retained mode rendering pattern, which is a system in which graphics commands are recorded (retained) to execute in succession at a later time. This is contrary to immediate mode, where graphics commands are immediately executed on client calls. == Usage in Direct3D 12 == Command lists are created using the ID3D12Device::CreateCommandList function. Command lists may be created in several types: direct, bundle, compute, copy, video decode, video process, and video encoding. Direct command lists specify that a command list the GPU can execute, and doesn't inherit any GPU state. Bundles, are best used for storing and executing small sets of commands any number of times. This is used differently than regular command lists, where commands stored in a command list are typically executed only once. Compute command lists are used for general computations, with a common use being calculating mipmaps. A copy command list is strictly for copying and the video decode and video process command lists are for video decoding and processing respectively. Upon creation, command lists are in the recording state. Command lists may be re-used by calling the ID3D12GraphicsCommandList::Reset function. After recording commands, the command list must be transitioned out of the recording state by calling ID3D12GraphicsCommandList::Close. The command list is then executed by calling ID3D12CommandQueue::ExecuteCommandLists.
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